Image filtering, i.e., restraining noises of an image while preserving specific characteristics as more as possible of the image, is a necessary operation in image processing, and its processing result directly affects effectiveness and reliability of following image processing and analysis.
Due to imperfection of imaging systems, transmission mediums and recording devices and so on, digital images usually suffer from pollutions of various noises when they are formed, transmitted and recorded. Additionally, in some image processing steps, when an input object is worse than expected in image quality, noises also would be introduced to a result image. These noises usually appear as isolated pixels or pixel blocks that would cause comparatively strong visual effect on an image. For digital image signals, noises usually appear as maximum or minimum extreme values. The extreme values affect real gray values of image pixels through adding or subtracting computation, which results in bright or dim spot interferences on the image, greatly degrades the quality of the image and affects subsequent works such as image restoration, segmentation, feature extraction, image identification and so on.
To construct a filter capable of restraining noises effectively, the following two basic problems must be taken into consideration: (1) removing noises in an image effectively; and (2) preserving shape, size, geometry and topology characteristics of the image.
Edge-preserving filtering technique is widely used in digital image processing. This technique can effectively preserve edge information of an image while filtering the image. Traditional edge-persevering filters comprise bilateral filters, weighted least squares (WLS) filters and so on. The computation of these filters is relatively complex, so it is difficult for the filters to implement real-time processing.